Evaluation and identification of clinical measurements in case of type 1 diabetes

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Dr. Kovács Levente
Department of Control Engineering and Information Technology

Diabetes mellitus (DM), or also known as diabetes, is a common disease of our modern world. Extensive researches on diabetes have been done worldwide to be able to treat and cure the disease. Unfortunately, in spite of the numerous researches, DM is not curable, however, outstanding representatives of different disciplines have already cooperated to improve the lives of people living with diabetes in some way.

Artificial pancreas makes the insulin intake automated by measuring blood glucose concentration frequently with CGMS, so the hypoglycemia (excessively low level of blood glucose) evolving due to the illness can be avoided and the incidence of hyperglycemia (excessively high level of blood glucose) can be decreased.

Writing my thesis, as the continuation of my Project laboratory work, my first task was to process data series from several hospitals. I had to pay increased attention to recognize incidental failures, such as incorrect measurement data, missing carbohydrate or insulin values and their correction when it was necessary. I proceeded the data in a MATLAB environment. Finishing the script, I studied the basics of linear identification and identified ARX model on the previously proceeded data series. Comparing the outputs of the models I received with the measured data I modified the parametres of the identification. I validated the models on data series with unequal length and I demonstrated their „goodness” by using Fit values and figures. After analyising the received models from physiological point of view, I realised that the mathematical models giving the best approximations differed in several characteristics from the ones which had been expected on the basis of the physiological knowledge.

The models in my thesis can provide a good basis for applying more precise and complicated identification methods in the future.


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